COMPARISON OF TWO NOVEL LIST SPHERE DETECTOR ALGORITHMS FOR MIMO-OFDM SYSTEMS

In this paper, the complexity and performance of two novel list sphere detector (LSD) algorithms are studied and evaluated in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system. The LSDs are based on the K-best and the Schnorr-Euchner enumeration (SEE) algorithms. The required list sizes for LSD algorithms are determined for a 2×2 system with 4- quadrature amplitude modulation (QAM), 16-QAM, and 64-QAM. The complexity of the algorithms is compared by studying the number of visited nodes per received symbol vector by the algorithm in computer simulations. The SEE based LSD algorithm is found to be a less complex and a feasible choice for implementation compared to the K-best based LSD algorithm.ElekrobitNokiaTexas InstrumentsFinnish Funding Agency for Technology and InnovationTeke

Seasonal patterns and controls on net ecosystem CO2 exchange in a boreal peatland complex

We measured seasonal patterns of net ecosystem exchange (NEE) of CO2 in a diverse peatland complex underlain by discontinuous permafrost in northern Manitoba, Canada, as part of the Boreal Ecosystems Atmosphere Study (BOREAS). Study sites spanned the full range of peatland trophic and moisture gradients found in boreal environments from bog (pH 3.9) to rich fen (pH 7.2). During midseason (July‐August, 1996), highest rates of NEE and respiration followed the trophic sequence of bog (5.4 to −3.9 μmol CO2 m−2 s−1) \u3c poor fen (6.3 to −6.5 μmol CO2 m−2 s−1) \u3c intermediate fen (10.5 to −7.8 μmol CO2 m−2 s−1) \u3c rich fen (14.9 to −8.7 μmol CO2m−2 s−1). The sequence changed during spring (May‐June) and fall (September‐October) when ericaceous shrub (e.g., Chamaedaphne calyculata) bogs and sedge (Carex spp.) communities in poor to intermediate fens had higher maximum CO2 fixation rates than deciduous shrub‐dominated (Salix spp. and Betula spp.) rich fens. Timing of snowmelt and differential rates of peat surface thaw in microtopographic hummocks and hollows controlled the onset of carbon uptake in spring. Maximum photosynthesis and respiration were closely correlated throughout the growing season with a ratio of approximately 1/3 ecosystem respiration to maximum carbon uptake at all sites across the trophic gradient. Soil temperatures above the water table and timing of surface thaw and freeze‐up in the spring and fall were more important to net CO2 exchange than deep soil warming. This close coupling of maximum CO2 uptake and respiration to easily measurable variables, such as trophic status, peat temperature, and water table, will improve models of wetland carbon exchange. Although trophic status, aboveground net primary productivity, and surface temperatures were more important than water level in predicting respiration on a daily basis, the mean position of the water table was a good predictor (r2 = 0.63) of mean respiration rates across the range of plant community and moisture gradients. Q10 values ranged from 3.0 to 4.1 from bog to rich fen, but when normalized by above ground vascular plant biomass, the Q10 for all sites was 3.3

Relationship between ecosystem productivity and photosynthetically-active radiation for northern peatlands

We analyzed the relationship between net ecosystem exchange of carbon dioxide (NEE) and irradiance (as photosynthetic photon flux density or PPFD), using published and unpublished data that have been collected during midgrowing season for carbon balance studies at seven peatlands in North America and Europe. NEE measurements included both eddy-correlation tower and clear, static chamber methods, which gave very similar results. Data were analyzed by site, as aggregated data sets by peatland type (bog, poor fen, rich fen, and all fens) and as a single aggregated data set for all peatlands. In all cases, a fit with a rectangular hyperbola (NEE = α PPFD Pmax/(α PPFD + Pmax) + R) better described the NEE-PPFD relationship than did a linear fit (NEE = β PPFD + R). Poor and rich fens generally had similar NEE-PPFD relationships, while bogs had lower respiration rates (R = −2.0μmol m−2s−1 for bogs and −2.7 μmol m−2s−1 for fens) and lower NEE at moderate and high light levels (Pmax = 5.2 μmol m−2s−1 for bogs and 10.8 μmol m−2s−1 for fens). As a single class, northern peatlands had much smaller ecosystem respiration (R = −2.4 μmol m−2s−1) and NEE rates (α = 0.020 and Pmax = 9.2μmol m−2s−1) than the upland ecosystems (closed canopy forest, grassland, and cropland) summarized by Ruimy et al. [1995]. Despite this low productivity, northern peatland soil carbon pools are generally 5–50 times larger than upland ecosystems because of slow rates of decomposition caused by litter quality and anaerobic, cold soils

High potential for weathering and climate effects of non-vascular vegetation in the Late Ordovician

It has been hypothesized that predecessors of today’s bryophytes significantly increased global chemical weathering in the Late Ordovician, thus reducing atmospheric CO2 concentration and contributing to climate cooling and an interval of glaciations. Studies that try to quantify the enhancement of weathering by non-vascular vegetation, however, are usually limited to small areas and low numbers of species, which hampers extrapolating to the global scale and to past climatic conditions. Here we present a spatially explicit modelling approach to simulate global weathering by non-vascular vegetation in the Late Ordovician. We estimate a potential global weathering flux of 2.8 (km3 rock) yr−1, defined here as volume of primary minerals affected by chemical transformation. This is around three times larger than today’s global chemical weathering flux. Moreover, we find that simulated weathering is highly sensitive to atmospheric CO2 concentration. This implies a strong negative feedback between weathering by non-vascular vegetation and Ordovician climate

Quantification of spatial pharmacogene expression heterogeneity in breast tumors.

BACKGROUND: Chemotherapeutic drug concentrations vary across different regions of tumors and this is thought to be involved in development of chemotherapy resistance. Insufficient drug delivery to some regions of the tumor may be due to spatial differences in expression of genes involved in the disposition, transport, and detoxification of drugs (pharmacogenes). Therefore, in this study, we analyzed the spatial expression of 286 pharmacogenes in six breast cancer tissues using the recently developed Visium spatial transcriptomics platform to (1) determine if these pharmacogenes are expressed heterogeneously across tumor tissue and (2) to determine which pharmacogenes have the most spatial expression heterogeneity. METHODS AND RESULTS: The spatial transcriptomics technology sequences the transcriptome of 55 um diameter barcoded sections (spots) across a tissue sample. We analyzed spatial gene expression profiles of four biobank-sourced breast tumor samples in addition to two breast tumor sample datasets from 10× Genomics. We define heterogeneity as the interquartile range of read counts. Collectively, we identified 8887 spots in tumor regions, 3814 in stroma, 44 in lymphocytes, and 116 in normal regions based on pathologist annotation of the tissues. We showed statistically significant differences in expression of pharmacogenes in tumor regions compared to surrounding non-tumor regions. We also observed that the most heterogeneously expressed genes within tumor regions were involved in reactive oxygen species (ROS) handling and detoxification mechanisms. GPX4, GSTP1, MGST3, SOD1, CYP4Z1, CYB5R3, GSTK1, and NAT1 showed the most heterogeneous expression within tumor regions. CONCLUSIONS: The heterogeneous expression of these pharmacogenes may have important implications for cancer therapy due to their ability to impact drug distribution and efficacy throughout the tumor. Our results suggest that chemoresistance caused by expression of GPX4, GSTP1, MGST3, and SOD1 may be intrinsic, not acquired, since the heterogeneity is not specific to chemotherapy-treated samples or cell type. Additionally, we identified candidate chemoresistance pharmacogenes that can be further tested through focused follow-up studies

CO2 fertilization of Sphagnum peat mosses is modulated by water table level and other environmental factors

Sphagnum mosses account for most accumulated dead organic matter in peatlands. Therefore, understanding their responses to increasing atmospheric CO2 is needed for estimating peatland C balances under climate change. A key process is photorespiration: a major determinant of net photosynthetic C assimilation that depends on the CO2 to O-2 ratio. We used climate chambers to investigate photorespiratory responses of Sphagnum fuscum hummocks to recent increases in atmospheric CO2 (from 280 to 400 ppm) under different water table, temperature, and light intensity levels. We tested the photorespiratory variability using a novel method based on deuterium isotopomers (D6(S)/D6(R) ratio) of photosynthetic glucose. The effect of elevated CO2 on photorespiration was highly dependent on water table. At low water table (-20 cm), elevated CO2 suppressed photorespiration relative to C assimilation, thus substantially increasing the net primary production potential. In contrast, a high water table (similar to 0 cm) favored photorespiration and abolished this CO2 effect. The response was further tested for Sphagnum majus lawns at typical water table levels (similar to 0 and -7 cm), revealing no effect of CO2 under those conditions. Our results indicate that hummocks, which typically experience low water table levels, benefit from the 20th century's increase in atmospheric CO2

Application of machine learning to predict reduction in total PANSS score and enrich enrollment in schizophrenia clinical trials

Clinical trial efficiency, defined as facilitating patient enrollment, and reducing the time to reach safety and efficacy decision points, is a critical driving factor for making improvements in therapeutic development. The present work evaluated a machine learning (ML) approach to improve phase II or proof-of-concept trials designed to address unmet medical needs in treating schizophrenia. Diagnostic data from the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) trial were used to develop a binary classification ML model predicting individual patient response as either "improvement," defined as greater than 20% reduction in total Positive and Negative Syndrome Scale (PANSS) score, or "no improvement," defined as an inadequate treatment response (<20% reduction in total PANSS). A random forest algorithm performed best relative to other tree-based approaches in model ability to classify patients after 6 months of treatment. Although model ability to identify true positives, a measure of model sensitivity, was poor (<0.2), its specificity, true negative rate, was high (0.948). A second model, adapted from the first, was subsequently applied as a proof-of-concept for the ML approach to supplement trial enrollment by identifying patients not expected to improve based on their baseline diagnostic scores. In three virtual trials applying this screening approach, the percentage of patients predicted to improve ranged from 46% to 48%, consistently approximately double the CATIE response rate of 22%. These results show the promising application of ML to improve clinical trial efficiency and, as such, ML models merit further consideration and development

VEGF-B-induced vascular growth leads to metabolic reprogramming and ischemia resistance in the heart

Peer reviewe

Comparison of Ga-68-DOTA-Siglec-9 and F-18-Fluorodeoxyribose-Siglec-9: Inflammation Imaging and Radiation Dosimetry

Sialic acid-binding immunoglobulin-like lectin 9 (Siglec-9) is a ligand of inflammation-inducible vascular adhesion protein-1 (VAP1). We compared Ga-68-DOTA-and F-18-fluorodeoxyribose-(FDR) labeled Siglec-9motif peptides for PET imaging of inflammation. Methods. Firstly, we examined Ga-68-DOTA-Siglec-9 and F-18-FDR-Siglec-9 in rats with skin/muscle inflammation. We then studied F-18-FDR-Siglec-9 for the detection of inflamed atherosclerotic plaques in mice and compared it with previous Ga-68-DOTA-Siglec-9 results. Lastly, we estimated human radiation dosimetry fromthe rat data. Results. In rats, Ga-68-DOTA-Siglec-9 (SUV, 0.88 +/- 0.087) and F-18-FDR-Siglec-9 (SUV, 0.77 +/- 0.22) showed comparable (P = 0.29) imaging of inflammation. In atherosclerotic mice, 18 FFDR- Siglec-9 detected inflamed plaques with a target-to-background ratio (1.6 1/8 0.078) similar to previously tested Ga-68-DOTASiglec- 9 (P = 0.35). Humaneffectivedose estimates for Ga-68-DOTA-Siglec-9 and (18) F-FDR-Siglec-9were 0.024 and 0.022 mSv/MBq, respectively. Conclusion. Both tracers are suitable for PET imaging of inflammation. The easier production and lower cost of (68)GaDOTA-Siglec-9 present advantages over F-18-FDR-Siglec-9, indicating it as a primary choice for clinical studies

Integrated time-lapse geoelectrical imaging of wetland hydrological processes

Wetlands provide crucial habitats, are critical in the global carbon cycle, and act as key biogeochemical and hydrological buffers. The effectiveness of these services is mainly controlled by hydrological processes, which can be highly variable both spatially and temporally due to structural complexity and seasonality. Spatial analysis of 2D geoelectrical monitoring data integrated into the interpretation of conventional hydrological data has been implemented to provide a detailed understanding of hydrological processes in a riparian wetland. This study shows that a combination of processes can define the resistivity signature of the shallow subsurface, highlighting the seasonality of these processes and its corresponding effect on biogeochemical processesthe wetland hydrology. Groundwater exchange between peat and the underlying river terrace deposits, spatially and temporally defined by geoelectrical imaging and verified by point sensor data, highlighted the groundwater dependent nature of the wetland. A 30 % increase in peat resistivity was shown to be caused by a nearly entire exchange of the saturating groundwater. For the first time, we showed that automated interpretation of geoelectrical data can be used to quantify shrink-swell of expandable soils, affecting hydrological parameters, such as, porosity, water storage capacity, and permeability. This study shows that an integrated interpretation of hydrological and geophysical data can significantly improve the understanding of wetland hydrological processes. Potentially, this approach can provide the basis for the evaluation of ecosystem services and may aid in the optimization of wetland management strategies